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    xxxxxxxxxx
    ​

    Usage Guidelines

    This lesson is part of the DS Lab core curriculum. For that reason, this notebook can only be used on your WQU virtual machine.

    This means:

    • ⓧ No downloading this notebook.
    • ⓧ No re-sharing of this notebook with friends or colleagues.
    • ⓧ No downloading the embedded videos in this notebook.
    • ⓧ No re-sharing embedded videos with friends or colleagues.
    • ⓧ No adding this notebook to public or private repositories.
    • ⓧ No uploading this notebook (or screenshots of it) to other websites, including websites for study resources.

    xxxxxxxxxx
    <font size="+3"><strong>Visualizing Data: plotly express</strong></font>

    Visualizing Data: plotly express

    xxxxxxxxxx
    There are many ways to interact with data, and one of the most powerful modes of interaction is through **visualizations**. Visualizations show data graphically, and are useful for exploring, analyzing, and presenting datasets. We use four libraries for making visualizations: [pandas](../%40textbook/07-visualization-pandas.ipynb), [Matplotlib](../%40textbook/06-visualization-matplotlib.ipynb), plotly express, and [seaborn](../%40textbook/09-visualization-seaborn.ipynb). In this section, we'll focus on using plotly express.

    There are many ways to interact with data, and one of the most powerful modes of interaction is through visualizations. Visualizations show data graphically, and are useful for exploring, analyzing, and presenting datasets. We use four libraries for making visualizations: pandas, Matplotlib, plotly express, and seaborn. In this section, we'll focus on using plotly express.

    xxxxxxxxxx
    # Scatter Plots

    Scatter Plots¶

    xxxxxxxxxx
    A **scatter plot** is a graph that uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables, and are especially useful if you're looking for **correlations**.

    A scatter plot is a graph that uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables, and are especially useful if you're looking for correlations.

    [1]:
     
    import pandas as pd
    ​
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    ​
    # clean the data and drop `NaNs`
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    mexico_city1 = mexico_city1.dropna(axis=0)
    mexico_city1.head()
    [1]:
    operation property_type place_with_parent_names lat-lon price currency price_aprox_local_currency price_aprox_usd surface_total_in_m2 surface_covered_in_m2 price_per_m2 properati_url
    2 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 2700000.0 MXN 2748947.10 146154.51 61.0 61.0 44262.295082 http://cuauhtemoc.properati.com.mx/2pu_venta_a...
    3 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 6347000.0 MXN 6462061.92 343571.36 176.0 128.0 49585.937500 http://cuauhtemoc.properati.com.mx/2pv_venta_a...
    6 sell apartment |México|Distrito Federal|Miguel Hidalgo| 19.456564,-99.191724 670000.0 MXN 682146.11 36267.97 65.0 65.0 10307.692308 http://miguel-hidalgo-df.properati.com.mx/46h_...
    7 sell apartment |México|Distrito Federal|Gustavo A. Madero| 19.512787,-99.141393 1400000.0 MXN 1425379.97 75783.82 82.0 70.0 20000.000000 http://gustavo-a-madero.properati.com.mx/46p_v...
    8 sell house |México|Distrito Federal|Álvaro Obregón| 19.358776,-99.213557 6680000.0 MXN 6801098.67 361597.08 346.0 346.0 19306.358382 http://alvaro-obregon.properati.com.mx/46t_ven...
    xxxxxxxxxx
    After cleaning the data, we can use plotly express to draw scatter plots by specifying the DataFrame and the interested column names.

    After cleaning the data, we can use plotly express to draw scatter plots by specifying the DataFrame and the interested column names.

    [2]:
     
    import plotly.express as px
    ​
    fig = px.scatter(mexico_city1, x="price", y="surface_covered_in_m2")
    fig.show()
    020M40M60M80M100M120M02000400060008000
    pricesurface_covered_in_m2
    plotly-logomark
    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Plot the scatter plot for column "price" and "surface_total_in_m2".

    [3]:
     
    fig = px.scatter(mexico_city1,x="price",y="surface_covered_in_m2")
    fig.show()
    020M40M60M80M100M120M02000400060008000
    pricesurface_covered_in_m2
    plotly-logomark
    xxxxxxxxxx
    # 3D Scatter Plots

    3D Scatter Plots¶

    Scatter plots can summarize information in a DataFrame. Three dimensional scatter plots look great, but be careful: it can be difficult for people who might not be sure what they're looking at to accurately determine values of points in the plot. Still, scatter plots are useful for displaying relationships between three quantities that would be more difficult to observe in a two dimensional plot.

    Let's take a look at the first 50 rows of the mexico-city-real-estate-1.csv dataset.

    [4]:
     
    import pandas as pd
    import plotly.express as px
    ​
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    mexico_city1 = mexico_city1.dropna(axis=0)
    mexico_city1[
        ["First Empty", "Country", "City", "Borough", "Second Empty"]
    ] = mexico_city1["place_with_parent_names"].str.split("|", 4, expand=True)
    mexico_city1 = mexico_city1.drop(["First Empty", "Second Empty"], axis=1)
    mexico_city1_subset = mexico_city1.loc[1:50]
    ​
    fig = px.scatter_3d(
        mexico_city1_subset,
        x="Borough",
        y="surface_covered_in_m2",
        z="price",
        symbol="property_type",
        color="property_type",
        labels={
            "surface_covered_in_m2": "Surface Covered in m^2",
            "price": "Price",
            "property_type": "Property Type",
        },
    )
    ​
    fig.show()
    /tmp/ipykernel_659/3122900234.py:11: FutureWarning:
    
    In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.
    
    
    Property Typeapartmenthousestore
    plotly-logomark
    xxxxxxxxxx
    Notice that the plot is interactive: you can rotate it zoom in or out. These kinds of plots also makes outliers easier to find; here, we can see that houses have higher prices than other types of properties.

    Notice that the plot is interactive: you can rotate it zoom in or out. These kinds of plots also makes outliers easier to find; here, we can see that houses have higher prices than other types of properties.

    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Modify the DataFrame to include columns for the base 10 log of price and surface_covered_in_m2 and then plot these for the entire mexico-city-real-estate-1.csv dataset.

    [5]:
     
    import math
    ​
    ​
    xxxxxxxxxx
    # Mapbox Scatter Plots

    Mapbox Scatter Plots¶

    xxxxxxxxxx
    A **mapbox scatter plot** is a special kind of scatter plot that allows you to create scatter plots in two dimensions and then superimpose them on top of a map. Our `mexico-city-real-estate-1.csv` dataset is a good place to start, because it includes **location data**. After importing the dataset and removing rows with missing data, split the `lat-lon` column into two separate columns: one for `latitude` and the other for `longitude`. Then use these to make a mapbox plot. Unfortunately, at present this type of plot does not easily allow for marker shape to vary based on a column of the DataFrame.

    A mapbox scatter plot is a special kind of scatter plot that allows you to create scatter plots in two dimensions and then superimpose them on top of a map. Our mexico-city-real-estate-1.csv dataset is a good place to start, because it includes location data. After importing the dataset and removing rows with missing data, split the lat-lon column into two separate columns: one for latitude and the other for longitude. Then use these to make a mapbox plot. Unfortunately, at present this type of plot does not easily allow for marker shape to vary based on a column of the DataFrame.

    [6]:
     
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    mexico_city1 = mexico_city1.dropna(axis=0)
    mexico_city1[["latitude", "longitude"]] = mexico_city1["lat-lon"].str.split(
        ",", 2, expand=True
    )
    mexico_city1["latitude"] = mexico_city1["latitude"].astype(float)
    mexico_city1["longitude"] = mexico_city1["longitude"].astype(float)
    fig = px.scatter_mapbox(
        mexico_city1,
        lat="latitude",
        lon="longitude",
        color="property_type",
        mapbox_style="carto-positron",
        labels={"property_type": "Property Type"},
        title="Distribution of Property Types for Sale in Mexico City",
    )
    fig.show()
    /tmp/ipykernel_659/3692783844.py:6: FutureWarning:
    
    In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.
    
    
    © Carto © OpenStreetMap contributors
    Property TypeapartmenthousestoreDistribution of Property Types for Sale in Mexico City
    plotly-logomark
    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Create another column in the DataFrame with a log scale of the prices. Then create three separate plots, one for stores, another for houses, and a final one for apartments. Color the points in the plots by the log of the price.

    [7]:
     
    from math import log10
    ​
    ​
    xxxxxxxxxx
    # Choropleth Maps

    Choropleth Maps¶

    xxxxxxxxxx
    A Choropleth Map is a map composed of colored polygons, showing the variable of interest at different color depth across geographies.Plotly express has a function called `px.choropleth` that be used to plot Choropleth Map. The challenges here are getting the geometry information. There are two ways, one is to use the built-in geometries in plotly when plot US States (use the state name directly) and world countries (use ISP-3 code). Another way is to look for GeoJSON files where each location has geometry information. In the following example, we will show the plot in US States with a synthetic data set.  

    A Choropleth Map is a map composed of colored polygons, showing the variable of interest at different color depth across geographies.Plotly express has a function called px.choropleth that be used to plot Choropleth Map. The challenges here are getting the geometry information. There are two ways, one is to use the built-in geometries in plotly when plot US States (use the state name directly) and world countries (use ISP-3 code). Another way is to look for GeoJSON files where each location has geometry information. In the following example, we will show the plot in US States with a synthetic data set.

    [8]:
    xxxxxxxxxx
     
    # Create Synthetic Dataset
    df = pd.DataFrame.from_dict(
        {"State": ["CA", "TX", "NY", "HI", "DE"], "Temparature": [100, 120, 110, 90, 105]}
    )
    df
    [8]:
    State Temparature
    0 CA 100
    1 TX 120
    2 NY 110
    3 HI 90
    4 DE 105
    [9]:
    xxxxxxxxxx
     
    # Plot the data set in US map
    fig = px.choropleth(
        df, locations="State", locationmode="USA-states", color="Temparature", scope="usa"
    )
    fig.show()
    90100110120Temparature
    plotly-logomark
    xxxxxxxxxx
    # Histogram

    Histogram¶

    xxxxxxxxxx
    A **histogram** is a graph that shows the frequency distribution of numerical data. In addition to helping us understand frequency, histograms are also useful for detecting outliers. We can use the `px.histogram()` function from Plotly to draw histograms for specific columns, as long as the data type is numerical. Let's check the following example:

    A histogram is a graph that shows the frequency distribution of numerical data. In addition to helping us understand frequency, histograms are also useful for detecting outliers. We can use the px.histogram() function from Plotly to draw histograms for specific columns, as long as the data type is numerical. Let's check the following example:

    [10]:
    xxxxxxxxxx
     
    import plotly.express as px
    ​
    df = pd.read_csv("data/mexico-city-real-estate-1.csv")
    fig = px.histogram(df, x="price")
    fig.show()
    020M40M60M80M100M120M0200400600800
    pricecount
    plotly-logomark
    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Check the "surface_covered_in_m2" Histogram.

    [11]:
    xxxxxxxxxx
     
    fig = px.histogram(df,x="surface_covered_in_m2")
    fig.show()
    01000200030004000500060007000800002004006008001000
    surface_covered_in_m2count
    plotly-logomark
    xxxxxxxxxx
    # Boxplots

    Boxplots¶

    xxxxxxxxxx
    A **boxplot** is a graph that shows the minimum, first quartile, median, third quartile, and the maximum values in a dataset. Boxplots are useful because they provide a visual summary of the data, enabling researchers to quickly identify mean values, the dispersion of the data set, and signs of skewness. In the following example, we will explore how to draw boxplots for specific columns of a DataFrame.

    A boxplot is a graph that shows the minimum, first quartile, median, third quartile, and the maximum values in a dataset. Boxplots are useful because they provide a visual summary of the data, enabling researchers to quickly identify mean values, the dispersion of the data set, and signs of skewness. In the following example, we will explore how to draw boxplots for specific columns of a DataFrame.

    [12]:
    xxxxxxxxxx
     
    # Read Data
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    ​
    # Clean the data and drop `NaNs`
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    mexico_city1 = mexico_city1.dropna(axis=0)
    ​
    # Exclude some outliers
    mexico_city1 = mexico_city1[mexico_city1["price"] < 100000000]
    mexico_city1.head()
    [12]:
    operation property_type place_with_parent_names lat-lon price currency price_aprox_local_currency price_aprox_usd surface_total_in_m2 surface_covered_in_m2 price_per_m2 properati_url
    2 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 2700000.0 MXN 2748947.10 146154.51 61.0 61.0 44262.295082 http://cuauhtemoc.properati.com.mx/2pu_venta_a...
    3 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 6347000.0 MXN 6462061.92 343571.36 176.0 128.0 49585.937500 http://cuauhtemoc.properati.com.mx/2pv_venta_a...
    6 sell apartment |México|Distrito Federal|Miguel Hidalgo| 19.456564,-99.191724 670000.0 MXN 682146.11 36267.97 65.0 65.0 10307.692308 http://miguel-hidalgo-df.properati.com.mx/46h_...
    7 sell apartment |México|Distrito Federal|Gustavo A. Madero| 19.512787,-99.141393 1400000.0 MXN 1425379.97 75783.82 82.0 70.0 20000.000000 http://gustavo-a-madero.properati.com.mx/46p_v...
    8 sell house |México|Distrito Federal|Álvaro Obregón| 19.358776,-99.213557 6680000.0 MXN 6801098.67 361597.08 346.0 346.0 19306.358382 http://alvaro-obregon.properati.com.mx/46t_ven...
    xxxxxxxxxx
    Check the boxplot for column `"price"`:

    Check the boxplot for column "price":

    [13]:
    xxxxxxxxxx
     
    import plotly.express as px
    ​
    fig = px.box(mexico_city1, y="price")
    fig.show()
    020M40M60M
    price
    plotly-logomark
    xxxxxxxxxx
    If you want to check the distribution of a column value by different categories, defined by another categorical column, you can add an `x` argument to specify the name of the categorical column. In the following example, we check the price distribution across different property types:

    If you want to check the distribution of a column value by different categories, defined by another categorical column, you can add an x argument to specify the name of the categorical column. In the following example, we check the price distribution across different property types:

    [14]:
    xxxxxxxxxx
     
    fig = px.box(mexico_city1, x="property_type", y="price")
    fig.show()
    apartmenthousestore020M40M60M
    property_typeprice
    plotly-logomark
    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Check the "surface_covered_in_m2" distribution by property types.

    [15]:
    xxxxxxxxxx
     
    fig = ...
    fig.show()
    ---------------------------------------------------------------------------
    AttributeError                            Traceback (most recent call last)
    Cell In [15], line 2
          1 fig = ...
    ----> 2 fig.show()
    
    AttributeError: 'ellipsis' object has no attribute 'show'
    xxxxxxxxxx
    # Bar Chart

    Bar Chart¶

    xxxxxxxxxx
    A **bar chart** is a graph that shows all the values of a categorical variable in a dataset. They consist of an axis and a series of labeled horizontal or vertical bars. The bars depict frequencies of different values of a variable or simply the different values themselves. The numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale. 

    A bar chart is a graph that shows all the values of a categorical variable in a dataset. They consist of an axis and a series of labeled horizontal or vertical bars. The bars depict frequencies of different values of a variable or simply the different values themselves. The numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

    In the following example, we will see some bar plots based on the Mexico City real estate dataset. Specifically, we will count the number of observations in each borough and plot them. We first need to read the data set and extract Borough and other location information from column "place_with_parent_names".

    [ ]:
    xxxxxxxxxx
     
    # Read Data
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    ​
    # Clean the data and drop `NaNs`
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    ​
    # find location columns from place_with_parent_names
    mexico_city1[
        ["First Empty", "Country", "City", "Borough", "Second Empty"]
    ] = mexico_city1["place_with_parent_names"].str.split("|", 4, expand=True)
    mexico_city1 = mexico_city1.drop(["First Empty", "Second Empty"], axis=1)
    mexico_city1 = mexico_city1.dropna(axis=0)
    ​
    # Exclude some outliers
    mexico_city1 = mexico_city1[mexico_city1["price"] < 100000000]
    mexico_city1 = mexico_city1[mexico_city1["Borough"] != ""]
    ​
    mexico_city1.head()
    xxxxxxxxxx
    We can calculate the number of real estate showing in the data set by Borough using `value_counts()`, then plot it as bar plot:

    We can calculate the number of real estate showing in the data set by Borough using value_counts(), then plot it as bar plot:

    [ ]:
    xxxxxxxxxx
     
    # Use value_counts() to get the data
    mexico_city1["Borough"].value_counts()
    [ ]:
    xxxxxxxxxx
     
    # Plot value_counts() data
    fig = px.bar(mexico_city1["Borough"].value_counts())
    fig.show()
    xxxxxxxxxx
    We can plot more expressive bar plots by adding more arguments. For example, we can plot the number of observations by borough and property type. First of all, we need use `groupby` to calculate the aggregated counts for each Borough and property type combination:

    We can plot more expressive bar plots by adding more arguments. For example, we can plot the number of observations by borough and property type. First of all, we need use groupby to calculate the aggregated counts for each Borough and property type combination:

    [ ]:
    xxxxxxxxxx
     
    size_df = mexico_city1.groupby(["Borough", "property_type"], as_index=False).size()
    size_df.head()
    xxxxxxxxxx
    By specifying `x`, `y` and `color`, the following bar graph shows the total counts by Borough, with different property types showing in different colors. Note `y` has to be numerical, while `x` and `color` are usually categorical variables.<span style='color: transparent; font-size:1%'>WQU WorldQuant University Applied Data Science Lab QQQQ</span>

    By specifying x, y and color, the following bar graph shows the total counts by Borough, with different property types showing in different colors. Note y has to be numerical, while x and color are usually categorical variables.WQU WorldQuant University Applied Data Science Lab QQQQ

    [ ]:
    xxxxxxxxxx
     
    fig = px.bar(size_df, x="Borough", y="size", color="property_type", barmode="relative")
    fig.show()
    xxxxxxxxxx
    Note the argument `barmode` is specified as 'relative', which is also the default value. In this mode, bars are stacked above each other. We can also use 'overlay' where bars are drawn on top of each other.

    Note the argument barmode is specified as 'relative', which is also the default value. In this mode, bars are stacked above each other. We can also use 'overlay' where bars are drawn on top of each other.

    [ ]:
    xxxxxxxxxx
     
    fig = px.bar(size_df, x="Borough", y="size", color="property_type", barmode="overlay")
    fig.show()
    xxxxxxxxxx
    If we want bars to be placed beside each other, we can specify `barmode` as "group":

    If we want bars to be placed beside each other, we can specify barmode as "group":

    [ ]:
    xxxxxxxxxx
     
    fig = px.bar(size_df, x="Borough", y="size", color="property_type", barmode="group")
    fig.show()
    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Plot bar plot for the number of observations by property types in "mexico-city-real-estate-1.csv".

    [ ]:
    xxxxxxxxxx
     
    bar_df = ...
    ​
    fig = ...
    fig.show()
    xxxxxxxxxx
    # References and Further Reading

    References and Further Reading¶

    • Official plotly express Documentation on Scatter Plots
    • Official plotly Express Documentation on 3D Plots
    • Official plotly Documentation on Notebooks
    • plotly Community Forum Post on Axis Labeling
    • plotly express Official Documentation on Tile Maps
    • plotly Choropleth Maps in Python Document
    • plotly express Official Documentation on Figure Display
    • Online Tutorial on String Conversion in Pandas
    • Official Pandas Documentation on using Lambda Functions on a Column
    • Official Seaborn Documentation on Generating a Heatmap
    • Online Tutorial on Correlation Matrices in Pandas
    • Official Pandas Documentation on Correlation Matrices
    • Official Matplotlib Documentation on Colormaps
    • Official Pandas Documentation on Box Plots
    • Online Tutorial on Box Plots
    • Online Tutorial on Axes Labels in Seaborn and Matplotlib
    • Matplotlib Gallery Example of an Annotated Heatmap
    xxxxxxxxxx
    ---

    Copyright 2022 WorldQuant University. This content is licensed solely for personal use. Redistribution or publication of this material is strictly prohibited.

    xxxxxxxxxx
    ​

    Usage Guidelines

    This lesson is part of the DS Lab core curriculum. For that reason, this notebook can only be used on your WQU virtual machine.

    This means:

    • ⓧ No downloading this notebook.
    • ⓧ No re-sharing of this notebook with friends or colleagues.
    • ⓧ No downloading the embedded videos in this notebook.
    • ⓧ No re-sharing embedded videos with friends or colleagues.
    • ⓧ No adding this notebook to public or private repositories.
    • ⓧ No uploading this notebook (or screenshots of it) to other websites, including websites for study resources.

    xxxxxxxxxx
    <font size="+3"><strong>Visualizing Data: seaborn</strong></font>

    Visualizing Data: seaborn

    xxxxxxxxxx
    There are many ways to interact with data, and one of the most powerful modes of interaction is through **visualizations**. Visualizations show data graphically, and are useful for exploring, analyzing, and presenting datasets. We use four libraries for making visualizations: [pandas](../%40textbook/07-visualization-pandas.ipynb), [Matplotlib](../%40textbook/06-visualization-matplotlib.ipynb), [plotly express](../%40textbook/08-visualization-plotly.ipynb), and seaborn. In this section, we'll focus on using seaborn.

    There are many ways to interact with data, and one of the most powerful modes of interaction is through visualizations. Visualizations show data graphically, and are useful for exploring, analyzing, and presenting datasets. We use four libraries for making visualizations: pandas, Matplotlib, plotly express, and seaborn. In this section, we'll focus on using seaborn.

    xxxxxxxxxx
    # Scatter Plots

    Scatter Plots¶

    xxxxxxxxxx
    A **scatter plot** is a graph that uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables, and are especially useful if you're looking for **correlations**. 

    A scatter plot is a graph that uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables, and are especially useful if you're looking for correlations.

    In the following example, we will see some scatter plots based on the Mexico City real estate data. Specifically, we can use scatter plot to show how "price" and "surface_covered_in_m2" are correlated. First we need to read the data set and do a little cleaning.

    [1]:
     
    import pandas as pd
    import seaborn as sns
    ​
    # Read Data
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    ​
    # Clean the data and drop `NaNs`
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    ​
    mexico_city1 = mexico_city1.dropna(axis=0)
    ​
    # Exclude some outliers
    mexico_city1 = mexico_city1[mexico_city1["price"] < 100000000]
    ​
    mexico_city1.head()
    [1]:
    operation property_type place_with_parent_names lat-lon price currency price_aprox_local_currency price_aprox_usd surface_total_in_m2 surface_covered_in_m2 price_per_m2 properati_url
    2 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 2700000.0 MXN 2748947.10 146154.51 61.0 61.0 44262.295082 http://cuauhtemoc.properati.com.mx/2pu_venta_a...
    3 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 6347000.0 MXN 6462061.92 343571.36 176.0 128.0 49585.937500 http://cuauhtemoc.properati.com.mx/2pv_venta_a...
    6 sell apartment |México|Distrito Federal|Miguel Hidalgo| 19.456564,-99.191724 670000.0 MXN 682146.11 36267.97 65.0 65.0 10307.692308 http://miguel-hidalgo-df.properati.com.mx/46h_...
    7 sell apartment |México|Distrito Federal|Gustavo A. Madero| 19.512787,-99.141393 1400000.0 MXN 1425379.97 75783.82 82.0 70.0 20000.000000 http://gustavo-a-madero.properati.com.mx/46p_v...
    8 sell house |México|Distrito Federal|Álvaro Obregón| 19.358776,-99.213557 6680000.0 MXN 6801098.67 361597.08 346.0 346.0 19306.358382 http://alvaro-obregon.properati.com.mx/46t_ven...
    xxxxxxxxxx
    Use seaborn to plot the scatter plot for `"price"` and `"surface_covered_in_m2"`:

    Use seaborn to plot the scatter plot for "price" and "surface_covered_in_m2":

    [2]:
     
    sns.scatterplot(data=mexico_city1, x="price", y="surface_covered_in_m2");
    xxxxxxxxxx
    There is a very useful argument in `scatterplot` called `hue`. By specifying a categorical column as `hue`, seaborn can create a scatter plot between two variables in different categories with different colors. Let's check the following example using `"property_type"`:

    There is a very useful argument in scatterplot called hue. By specifying a categorical column as hue, seaborn can create a scatter plot between two variables in different categories with different colors. Let's check the following example using "property_type":

    [3]:
     
    sns.scatterplot(
        data=mexico_city1, x="price", y="surface_covered_in_m2", hue="property_type"
    );
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Plot a scatter plot for "price" and "surface_total_in_m2" by "property_type" for "mexico-city-real-estate-1.csv":

    [ ]:
     
    ​
    xxxxxxxxxx
    # Bar Charts

    Bar Charts¶

    xxxxxxxxxx
    A **bar chart** is a graph that shows all the values of a categorical variable in a dataset. They consist of an axis and a series of labeled horizontal or vertical bars. The bars depict frequencies of different values of a variable or simply the different values themselves. The numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale. 

    A bar chart is a graph that shows all the values of a categorical variable in a dataset. They consist of an axis and a series of labeled horizontal or vertical bars. The bars depict frequencies of different values of a variable or simply the different values themselves. The numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

    In the following example, we will see some bar plots based on the Mexico City real estate dataset. Specifically, we will count the number of observations in each borough and plot them. We first need to import the dataset and extract the borough and other location information from column "place_with_parent_names".

    [4]:
    xxxxxxxxxx
     
    # Read Data
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    ​
    # Clean the data and drop `NaNs`
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    ​
    # find location columns from place_with_parent_names
    mexico_city1[
        ["First Empty", "Country", "City", "Borough", "Second Empty"]
    ] = mexico_city1["place_with_parent_names"].str.split("|", 4, expand=True)
    mexico_city1 = mexico_city1.drop(["First Empty", "Second Empty"], axis=1)
    mexico_city1 = mexico_city1.dropna(axis=0)
    ​
    # Exclude some outliers
    mexico_city1 = mexico_city1[mexico_city1["price"] < 100000000]
    mexico_city1 = mexico_city1[mexico_city1["Borough"] != ""]
    ​
    mexico_city1.head()
    /tmp/ipykernel_721/836102575.py:12: FutureWarning: In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.
      ] = mexico_city1["place_with_parent_names"].str.split("|", 4, expand=True)
    
    [4]:
    operation property_type place_with_parent_names lat-lon price currency price_aprox_local_currency price_aprox_usd surface_total_in_m2 surface_covered_in_m2 price_per_m2 properati_url Country City Borough
    2 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 2700000.0 MXN 2748947.10 146154.51 61.0 61.0 44262.295082 http://cuauhtemoc.properati.com.mx/2pu_venta_a... México Distrito Federal Cuauhtémoc
    3 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 6347000.0 MXN 6462061.92 343571.36 176.0 128.0 49585.937500 http://cuauhtemoc.properati.com.mx/2pv_venta_a... México Distrito Federal Cuauhtémoc
    6 sell apartment |México|Distrito Federal|Miguel Hidalgo| 19.456564,-99.191724 670000.0 MXN 682146.11 36267.97 65.0 65.0 10307.692308 http://miguel-hidalgo-df.properati.com.mx/46h_... México Distrito Federal Miguel Hidalgo
    7 sell apartment |México|Distrito Federal|Gustavo A. Madero| 19.512787,-99.141393 1400000.0 MXN 1425379.97 75783.82 82.0 70.0 20000.000000 http://gustavo-a-madero.properati.com.mx/46p_v... México Distrito Federal Gustavo A. Madero
    8 sell house |México|Distrito Federal|Álvaro Obregón| 19.358776,-99.213557 6680000.0 MXN 6801098.67 361597.08 346.0 346.0 19306.358382 http://alvaro-obregon.properati.com.mx/46t_ven... México Distrito Federal Álvaro Obregón
    xxxxxxxxxx
    Let's check the example of a bar plot showing the value counts of each borough in the dataset. We first need to create a DataFrame showing the value counts:

    Let's check the example of a bar plot showing the value counts of each borough in the dataset. We first need to create a DataFrame showing the value counts:

    [5]:
     
    bar_df = pd.DataFrame(mexico_city1["Borough"].value_counts()).reset_index()
    bar_df
    [5]:
    index Borough
    0 Miguel Hidalgo 345
    1 Cuajimalpa de Morelos 255
    2 Álvaro Obregón 203
    3 Benito Juárez 198
    4 Tlalpan 171
    5 Iztapalapa 134
    6 Tláhuac 125
    7 Cuauhtémoc 120
    8 Gustavo A. Madero 89
    9 Venustiano Carranza 81
    10 Coyoacán 80
    11 La Magdalena Contreras 41
    12 Xochimilco 34
    13 Iztacalco 27
    14 Azcapotzalco 24
    15 Milpa Alta 1
    xxxxxxxxxx
    Since there are 16 different categories in Borough, we should increase the default plot size and rotate the x axis to make the plot more readable using the following syntax:

    Since there are 16 different categories in Borough, we should increase the default plot size and rotate the x axis to make the plot more readable using the following syntax:

    [6]:
     
    # Increase plot size
    sns.set(rc={"figure.figsize": (15, 4)})
    ​
    # Plot the bar plot
    ax = sns.barplot(data=bar_df, x="index", y="Borough")
    ​
    # Rotate the x axis
    ax.set_xticklabels(ax.get_xticklabels(), rotation=75)
    [6]:
    [Text(0, 0, 'Miguel Hidalgo'),
     Text(1, 0, 'Cuajimalpa de Morelos'),
     Text(2, 0, 'Álvaro Obregón'),
     Text(3, 0, 'Benito Juárez'),
     Text(4, 0, 'Tlalpan'),
     Text(5, 0, 'Iztapalapa'),
     Text(6, 0, 'Tláhuac'),
     Text(7, 0, 'Cuauhtémoc'),
     Text(8, 0, 'Gustavo A. Madero'),
     Text(9, 0, 'Venustiano Carranza'),
     Text(10, 0, 'Coyoacán'),
     Text(11, 0, 'La Magdalena Contreras'),
     Text(12, 0, 'Xochimilco'),
     Text(13, 0, 'Iztacalco'),
     Text(14, 0, 'Azcapotzalco'),
     Text(15, 0, 'Milpa Alta')]
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Plot a bar plot showing the value counts for property types in "mexico-city-real-estate-1.csv":

    [7]:
    xxxxxxxxxx
     
    pro_typ_df = pd.DataFrame(mexico_city1["property_type"].value_counts()).reset_index()
    pro_typ_df
    ​
    sns.barplot(data =pro_typ_df,x="index",y="property_type")
    ​
    [7]:
    <AxesSubplot:xlabel='index', ylabel='property_type'>
    xxxxxxxxxx
    # Correlation Heatmaps

    Correlation Heatmaps¶

    A correlation heatmap shows the relative strength of correlations between the variables in a dataset. Here's what the code looks like:

    [8]:
    xxxxxxxxxx
     
    import pandas as pd
    import seaborn as sns
    ​
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    mexico_city1 = mexico_city1.dropna(axis=0)
    mexico_city1_numeric = mexico_city1.select_dtypes(include="number")
    corr = mexico_city1_numeric.corr(method="kendall")
    sns.heatmap(corr)
    [8]:
    <AxesSubplot:>
    xxxxxxxxxx
    Notice that we dropped the columns and rows with missing entries before plotting the graph.

    Notice that we dropped the columns and rows with missing entries before plotting the graph.

    This heatmap is showing us what we might already have suspected: the price is moderately positively correlated with the size of the properties.

    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    The seaborn documentation on heat maps indicates how to add numeric labels to each cell and how to use a different colormap. Modify the plot to use the viridis colormap, have a linewidth of 0.5 between each cell and have numeric labels for each cell.

    [ ]:
    xxxxxxxxxx
     
    ​
    xxxxxxxxxx
    # References and Further Reading

    References and Further Reading¶

    • Official Plotly Express Documentation on Scatter Plots
    • Official Plotly Express Documentation on 3D Plots
    • Official Plotly Documentation on Notebooks
    • Plotly Community Forum Post on Axis Labeling
    • Plotly Express Official Documentation on Tile Maps
    • Plotly Express Official Documentation on Figure Display
    • Online Tutorial on String Conversion in Pandas
    • Official Pandas Documentation on using Lambda Functions on a Column
    • Official seaborn Documentation on Generating a Heatmap
    • Online Tutorial on Correlation Matrices in Pandas
    • Official Pandas Documentation on Correlation Matrices
    • Official Matplotlib Documentation on Colormaps
    • Official Pandas Documentation on Box Plots
    • Online Tutorial on Box Plots
    • Online Tutorial on Axes Labels in seaborn and Matplotlib
    • Matplotlib Gallery Example of an Annotated Heatmap
    xxxxxxxxxx
    ---

    Copyright 2022 WorldQuant University. This content is licensed solely for personal use. Redistribution or publication of this material is strictly prohibited. WQU WorldQuant University Applied Data Science Lab QQQQ

    xxxxxxxxxx
    ---

    Copyright 2022 WorldQuant University. This content is licensed solely for personal use. Redistribution or publication of this material is strictly prohibited.

    xxxxxxxxxx
    ​

    Usage Guidelines

    This lesson is part of the DS Lab core curriculum. For that reason, this notebook can only be used on your WQU virtual machine.

    This means:

    • ⓧ No downloading this notebook.
    • ⓧ No re-sharing of this notebook with friends or colleagues.
    • ⓧ No downloading the embedded videos in this notebook.
    • ⓧ No re-sharing embedded videos with friends or colleagues.
    • ⓧ No adding this notebook to public or private repositories.
    • ⓧ No uploading this notebook (or screenshots of it) to other websites, including websites for study resources.

    xxxxxxxxxx
    <font size="+3"><strong>Databases: SQL</strong></font>

    Databases: SQL

    [ ]:
     
    from IPython.display import YouTubeVideo
    xxxxxxxxxx
    # Working with SQL Databases

    Working with SQL Databases¶

    xxxxxxxxxx
    A database is a collection of interrelated data. The primary goal of a database is to store and retrieve information in a convenient and efficient way. There are many types of databases. In this section, we will be dealing with a **relational database**. A relational database is a widely used database model that consists of a collection of uniquely named **tables** used to store information. The structure of a database model with its tables, constraints, and relationships is called a **schema**. 

    A database is a collection of interrelated data. The primary goal of a database is to store and retrieve information in a convenient and efficient way. There are many types of databases. In this section, we will be dealing with a relational database. A relational database is a widely used database model that consists of a collection of uniquely named tables used to store information. The structure of a database model with its tables, constraints, and relationships is called a schema.

    A Structured Query Language (SQL), is used to retrieve information from a relational database. SQL is one of the most commonly used database languages. It allows data stored in a relational database to be queried, modified, and manipulated easily with basic commands. SQL powers database engines like MySQL, SQL Server, SQLite, and PostgreSQL. The examples and projects in this course will use SQLite.

    A table refers to a collection of rows and columns in a relational database. When reading data into a pandas DataFrame, an index can be defined, which acts as the label for every row in the DataFrame.

    xxxxxxxxxx
    # Connecting to a Database

    Connecting to a Database¶

    xxxxxxxxxx
    ## ipython-sql 

    ipython-sql¶

    xxxxxxxxxx
    ### Magic Commands

    Magic Commands¶

    xxxxxxxxxx
    Jupyter notebooks can run code that is not valid Python code but still affect the notebook . These special commands are called magic commands. Magic commands can have a range of properties. Some commonly used magic functions are below:

    Jupyter notebooks can run code that is not valid Python code but still affect the notebook . These special commands are called magic commands. Magic commands can have a range of properties. Some commonly used magic functions are below:

    Magic Command Description of Command
    %pwd Print the current working directory
    %cd Change the current working directory
    %ls List the contents of the current directory
    %history Show the history of the In [ ]: commands

    We will be leveraging magic commands to work with a SQLite database.

    xxxxxxxxxx
    ### ipython-sql

    ipython-sql¶

    xxxxxxxxxx
    `ipython-sql` allows you to write SQL code directly in a Jupyter Notebook. The `%sql` (or `%%sql`) magic command is added to the beginning of a code block and then SQL code can be written.

    ipython-sql allows you to write SQL code directly in a Jupyter Notebook. The %sql (or %%sql) magic command is added to the beginning of a code block and then SQL code can be written.

    xxxxxxxxxx
    ### Connecting with ipython-sql

    Connecting with ipython-sql¶

    xxxxxxxxxx
    We can connect to a database using the %sql magic function:

    We can connect to a database using the %sql magic function:

    [ ]:
     
    %load_ext sql
    %sql sqlite:////home/jovyan/nepal.sqlite
    xxxxxxxxxx
    ## sqlite3

    sqlite3¶

    xxxxxxxxxx
    We can also connect to the same database using the sqlite3 package:

    We can also connect to the same database using the sqlite3 package:

    [ ]:
     
    import sqlite3
    ​
    conn = sqlite3.connect("/home/jovyan/nepal.sqlite")
    xxxxxxxxxx
    # Querying a Database

    Querying a Database¶

    xxxxxxxxxx
    ## Building Blocks of the Basic Query

    Building Blocks of the Basic Query¶

    xxxxxxxxxx
    There are six common clauses used for querying data:

    There are six common clauses used for querying data:

    Clause Name Definition
    SELECT Determines which columns to include in the query's result
    FROM Identifies the table from which to query the data from
    WHERE filters data
    GROUP BY groups rows by common values in columns
    HAVING filters out unwanted groups from GROUP BY
    ORDER BY Orders the rows using one or more columns
    LIMIT Outputs the specified number of rows

    All clauses may be used together, but SELECT and FROM are the only required clauses. The format of clauses is in the example query below:

    SELECT column1, column2
    FROM table_name
    WHERE "conditions"
    GROUP BY "column-list"
    HAVING "conditions"
    ORDER BY "column-list"
    
    xxxxxxxxxx
    ## SELECT and FROM

    SELECT and FROM¶

    xxxxxxxxxx
    You can use `SELECT *` to select all columns in a table. `FROM` specifies the table in the database to query. `LIMIT 5` will select only the first five rows. 

    You can use SELECT * to select all columns in a table. FROM specifies the table in the database to query. LIMIT 5 will select only the first five rows.

    Example

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT *
    FROM id_map
    LIMIT 5
    xxxxxxxxxx
    You can also use `SELECT` to select certain columns in a table

    You can also use SELECT to select certain columns in a table

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT household_id,
           building_id
    FROM id_map
    LIMIT 5
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use SELECT to select the district_id column from the id_map table.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    We can also assign an **alias** or temporary name to a column using the `AS` command. Aliases can also be used on a table. See the example below, which assigns the alias `household_number` to `household_id`

    We can also assign an alias or temporary name to a column using the AS command. Aliases can also be used on a table. See the example below, which assigns the alias household_number to household_id

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT household_id AS household_number,
           building_id
    FROM id_map
    LIMIT 5
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use SELECT, FROM, AS, and LIMIT to select the first 5 rows from the id_map table. Rename the district_id column to district_number.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    ## Filtering and Sorting Data

    Filtering and Sorting Data¶

    xxxxxxxxxx
    SQL provides a variety of comparison operators that can be used with the WHERE clause to filter the data. 

    SQL provides a variety of comparison operators that can be used with the WHERE clause to filter the data.

    Comparison Operator Description
    = Equal
    > Greater than
    < Less than
    >= Greater than or equal to
    <= Less than or equal to
    <> or != Not equal to
    LIKE String comparison test
    xxxxxxxxxx
    For example, to select the first 5 homes in Ramechhap (district `2`):

    For example, to select the first 5 homes in Ramechhap (district 2):

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use WHERE to select the row with household_id equal to 13735001

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    ## Aggregating Data

    Aggregating Data¶

    xxxxxxxxxx
    Aggregation functions take a collection of values as inputs and return one value as the output. The table below gives the frequently used built-in aggregation functions:

    Aggregation functions take a collection of values as inputs and return one value as the output. The table below gives the frequently used built-in aggregation functions:

    Aggregation Function Definition
    MIN Return the minimum value
    MAX Return the largest value
    SUM Return the sum of values
    AVG Return the average of values
    COUNT Return the number of observations
    xxxxxxxxxx
    Use the `COUNT` function to find the number of observations in the `id_map` table that come from Ramechhap (district `2`):

    Use the COUNT function to find the number of observations in the id_map table that come from Ramechhap (district 2):

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT count(*)
    FROM id_map
    WHERE district_id = 2
    xxxxxxxxxx
    Aggregation functions are frequently used with a `GROUP BY` clause to perform the aggregation on groups of data. For example, the query below returns the count of observations in each District:

    Aggregation functions are frequently used with a GROUP BY clause to perform the aggregation on groups of data. For example, the query below returns the count of observations in each District:

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT district_id,
           count(*)
    FROM id_map
    GROUP BY district_id
    xxxxxxxxxx
     `DISTINCT` is a keyword to select unique records in a query result. For example, if we want to know the unique values in the `district_id` column:

    DISTINCT is a keyword to select unique records in a query result. For example, if we want to know the unique values in the district_id column:

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT distinct(district_id)
    FROM id_map
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use DISTINCT to count the number of unique values in the vdcmun_id column.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    `DISTINCT` and `COUNT` can be used in combination to count the number of distinct records. For example, if we want to know the number of unique values in the `district_id` column:

    DISTINCT and COUNT can be used in combination to count the number of distinct records. For example, if we want to know the number of unique values in the district_id column:

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT count(distinct(district_id))
    FROM id_map
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use DISTINCT and COUNT to count the number of unique values in the vdcmun_id column.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    # Joining Tables

    Joining Tables¶

    xxxxxxxxxx
    Joins link data from two or more tables together by using a column that is common between the two tables. The basic syntax for a join is below, where `table1` and `table2` refer to the two tables being joined, `column1` and `column2` refer to columns to be returned from both tables, and `ID` refers to the common column in the two tables. 

    Joins link data from two or more tables together by using a column that is common between the two tables. The basic syntax for a join is below, where table1 and table2 refer to the two tables being joined, column1 and column2 refer to columns to be returned from both tables, and ID refers to the common column in the two tables.

    SELECT table1.column1,
           table2.column2
    FROM table_1
    JOIN table2 ON table1.id = table1.id
    
    xxxxxxxxxx
    We'll explore the concept of joins by first identifying a single household that we'd like to pull in building information for. For example, let's say we want to see the corresponding `foundation_type` for the first home in Ramechhap (District 1). We'll start by looking at this single record in the `id_map` table.

    We'll explore the concept of joins by first identifying a single household that we'd like to pull in building information for. For example, let's say we want to see the corresponding foundation_type for the first home in Ramechhap (District 1). We'll start by looking at this single record in the id_map table.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT *
    FROM id_map
    WHERE district_id = 2
    LIMIT 1
    xxxxxxxxxx
    This household has `building_id` equal to 23. Let's look at the `foundation_type` for this building, by filtering the `building_structure` table to find this building.

    This household has building_id equal to 23. Let's look at the foundation_type for this building, by filtering the building_structure table to find this building.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT building_id,
           foundation_type
    FROM building_structure
    WHERE building_id = 23
    xxxxxxxxxx
    To join the two tables and limit the results to `building_id = 23`:    

    To join the two tables and limit the results to building_id = 23:

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT id_map.*,
           building_structure.foundation_type
    FROM id_map
    JOIN building_structure ON id_map.building_id = building_structure.building_id
    WHERE id_map.building_id = 23
    xxxxxxxxxx
    In addition to the basic `JOIN` clause, specific join types can be specified, which specify whether the common column needs to be in one, both, or either of the two tables being joined. The different join types are below. The left table is the table specified first, immediately after the `FROM` clause and the right table is the table specified after the `JOIN` clause. If the generic `JOIN` clause is used, then by default the `INNER JOIN` will be used.

    In addition to the basic JOIN clause, specific join types can be specified, which specify whether the common column needs to be in one, both, or either of the two tables being joined. The different join types are below. The left table is the table specified first, immediately after the FROM clause and the right table is the table specified after the JOIN clause. If the generic JOIN clause is used, then by default the INNER JOIN will be used.

    JOIN Type Definition
    INNER JOIN Returns rows where ID is in both tables
    LEFT JOIN Returns rows where ID is in the left table. Return NA for values in column, if ID is not in right table.
    RIGHT JOIN Returns rows where ID is in the right table. Return NA for values in column, if ID is not in left table.
    FULL JOIN Returns rows where ID is in either table. Return NA for values in column, if ID is not in either table.
    WQU WorldQuant University Applied Data Science Lab QQQQ
    xxxxxxxxxx
    The video below outlines the main types of joins:

    The video below outlines the main types of joins:

    [ ]:
    xxxxxxxxxx
     
    YouTubeVideo("2HVMiPPuPIM")
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use the DISTINCT command to create a column with all unique building IDs in the id_map table. LEFT JOIN this column with the roof_type column from the building_structure table, showing only buildings where district_id is 1 and limiting your results to the first five rows of the new table.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    # Using pandas with SQL Databases

    Using pandas with SQL Databases¶

    xxxxxxxxxx
    To save the output of a query into a pandas DataFrame, we will use connect to the SQLite database using the SQLite3 package:

    To save the output of a query into a pandas DataFrame, we will use connect to the SQLite database using the SQLite3 package:

    [ ]:
    xxxxxxxxxx
     
    import sqlite3
    ​
    conn = sqlite3.connect("/home/jovyan/nepal.sqlite")
    xxxxxxxxxx
    To run a query using `sqlite3`, we need to store the query as a string. For example, the variable below called `query` is a string containing a query which returns the first 10 rows from the `id_map` table:

    To run a query using sqlite3, we need to store the query as a string. For example, the variable below called query is a string containing a query which returns the first 10 rows from the id_map table:

    [ ]:
    xxxxxxxxxx
     
    query = """
        SELECT *
        FROM id_map
        LIMIT 10
        """
    xxxxxxxxxx
    To save the results of the query into a pandas DataFrame, use the `pd.read_sql()` function. The optional parameter `index_col` can be used to set the index to a specific column from the query. 

    To save the results of the query into a pandas DataFrame, use the pd.read_sql() function. The optional parameter index_col can be used to set the index to a specific column from the query.

    [ ]:
    xxxxxxxxxx
     
    import pandas as pd
    ​
    df = pd.read_sql(query, conn, index_col="building_id")
    ​
    df.head()
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use the pd.read_sql function to save the results of a query to a DataFrame. The query should select first 20 rows from the id_map table.

    [ ]:
    xxxxxxxxxx
     
    query = ...
    ​
    df2 = ...
    ​
    df2.head()
    xxxxxxxxxx
    # References & Further Reading

    References & Further Reading¶

    • Additional Explanation of Magic Commands
    • ipython-SQL User Documentation
    • Data Carpentry Course on SQL in Python
    • SQL Course Material on GitHub (1)
    • SQL Course Material on GitHub (2)
    xxxxxxxxxx
    ---

    Copyright 2022 WorldQuant University. This content is licensed solely for personal use. Redistribution or publication of this material is strictly prohibited.

    xxxxxxxxxx
    ​

    Usage Guidelines

    This lesson is part of the DS Lab core curriculum. For that reason, this notebook can only be used on your WQU virtual machine.

    This means:

    • ⓧ No downloading this notebook.
    • ⓧ No re-sharing of this notebook with friends or colleagues.
    • ⓧ No downloading the embedded videos in this notebook.
    • ⓧ No re-sharing embedded videos with friends or colleagues.
    • ⓧ No adding this notebook to public or private repositories.
    • ⓧ No uploading this notebook (or screenshots of it) to other websites, including websites for study resources.

    xxxxxxxxxx
    <font size="+3"><strong>Databases: PyMongo</strong></font>

    Databases: PyMongo

    xxxxxxxxxx
    # Working with PyMongo

    Working with PyMongo¶

    For all of these examples, we're going to be working with the "lagos" collection in the "air-quality" database. Before we can do anything else, we need to bring in pandas (which we won't use until the very end), pprint (a module that lets us see the data in an understandable way), and PyMongo (a library for working with MongoDB databases).

    [1]:
     
    from pprint import PrettyPrinter
    ​
    import pandas as pd
    from pymongo import MongoClient
    xxxxxxxxxx
    ## Databases

    Databases¶

    Data comes to us in lots of different ways, and one of those ways is in a database. A database is a collection of data.

    xxxxxxxxxx
    ## Servers and Clients

    Servers and Clients¶

    Next, we need to connect to a server. A database server is where the database resides; it can be accessed using a client. Without a client, a database is just a collection of information that we can't work with, because we have no way in. We're going to be learning more about a database called MongoDB, and we'll use PrettyPrinter to make the information it generates easier to understand. Here's how the connection works:

    [2]:
     
    pp = PrettyPrinter(indent=2)
    client = MongoClient(host="localhost", port=27017)
    xxxxxxxxxx
    ## Semi-structured Data

    Semi-structured Data¶

    Databases are designed to work with either structured data or semi-structured data. In this part of the course, we're going to be working with databases that contain semi-structured data. Data is semi-structured when it has some kind of organizing logic, but that logic can't be displayed using rows and columns. Your email account contains semi-structured data if it’s divided into sections like Inbox, Sent, and Trash. If you’ve ever seen tweets from Twitter grouped by hashtag, that’s semi-structured data too. Semi-structured data is also used in sensor readings, which is what we'll be working with here.

    xxxxxxxxxx
    ## Exploring a Database

    Exploring a Database¶

    So, now that we're connected to a server, let's take a look at what's there. Working our way down the specificity scale, the first thing we need to do is figure out which databases are on this server. To see which databases on the server, we'll use the list_databases method, like this:

    [3]:
     
    pp.pprint(list(client.list_databases()))
    [ {'empty': False, 'name': 'admin', 'sizeOnDisk': 40960},
      {'empty': False, 'name': 'air-quality', 'sizeOnDisk': 7000064},
      {'empty': False, 'name': 'config', 'sizeOnDisk': 12288},
      {'empty': False, 'name': 'local', 'sizeOnDisk': 73728},
      {'empty': False, 'name': 'wqu-abtest', 'sizeOnDisk': 585728}]
    
    xxxxxxxxxx
    It looks like this server contains four databases: `"admin"`, `"air-quality"`, `"config"`, and `"local"`. We're only interested in `"air-quality"`, so let's connect to that one:

    It looks like this server contains four databases: "admin", "air-quality", "config", and "local". We're only interested in "air-quality", so let's connect to that one:

    [4]:
     
    db = client["air-quality"]
    xxxxxxxxxx
    In MongoDB, a **database** is a container for **collections**. Each database gets its own set of files, and a single MongoDB **server** typically has multiple databases.

    In MongoDB, a database is a container for collections. Each database gets its own set of files, and a single MongoDB server typically has multiple databases.

    xxxxxxxxxx
    ## Collections

    Collections¶

    Let's use a for loop to take a look at the collections in the "air-quality" database:

    [5]:
     
    for c in db.list_collections():
        print(c["name"])
    system.views
    lagos
    system.buckets.lagos
    nairobi
    system.buckets.nairobi
    dar-es-salaam
    system.buckets.dar-es-salaam
    
    xxxxxxxxxx
    As you can see, there are three actual collections here: `"nairobi"`, `"lagos"`, and `"dar-es-salaam"`. Since we're only interested in the `"lagos"` collection, let's get it on its own like this: 

    As you can see, there are three actual collections here: "nairobi", "lagos", and "dar-es-salaam". Since we're only interested in the "lagos" collection, let's get it on its own like this:

    [6]:
     
    lagos = db["lagos"]
    xxxxxxxxxx
    ## Documents

    Documents¶

    xxxxxxxxxx
    A MongoDB **document** is an individual record of data in a **collection**, and is the basic unit of analysis in MongoDB. Documents come with **metadata** that helps us understand what the document is; we'll get back to that in a minute. In the meantime, let's use the [`count_documents`](https://pymongo.readthedocs.io/en/stable/api/pymongo/collection.html#pymongo.collection.Collection.count_documents) method to see how many documents the `"lagos"` collection contains:

    A MongoDB document is an individual record of data in a collection, and is the basic unit of analysis in MongoDB. Documents come with metadata that helps us understand what the document is; we'll get back to that in a minute. In the meantime, let's use the count_documents method to see how many documents the "lagos" collection contains:

    [7]:
     
    lagos.count_documents({})
    [7]:
    166496
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Bring in all the necessary libraries and modules, then connect to the "air-quality" database and print the number of documents in the "nairobi" collection.

    [8]:
    xxxxxxxxxx
     
    from pymongo import MongoClient
    from pprint import PrettyPrinter
    ​
    client = MongoClient(host = "localhost", port = 27017)
    pp = PrettyPrinter(indent = 5)
    ​
    pp.pprint(list(client.list_databases()))
    db = client["air-quality"]
    for c in db.list_collections():
        print(c["name"])
        
    nairobi = db["nairobi"]
    nairobi.count_documents({})
    ​
        
    [    {'empty': False, 'name': 'admin', 'sizeOnDisk': 40960},
         {'empty': False, 'name': 'air-quality', 'sizeOnDisk': 7000064},
         {'empty': False, 'name': 'config', 'sizeOnDisk': 12288},
         {'empty': False, 'name': 'local', 'sizeOnDisk': 73728},
         {'empty': False, 'name': 'wqu-abtest', 'sizeOnDisk': 585728}]
    system.views
    lagos
    system.buckets.lagos
    nairobi
    system.buckets.nairobi
    dar-es-salaam
    system.buckets.dar-es-salaam
    
    [8]:
    202212
    xxxxxxxxxx
    ### Retrieving Data

    Retrieving Data¶

    Now that we know how many documents the "lagos" collection contains, let's take a closer look at what's there. The first thing you'll notice is that the output starts out with a curly bracket ({), and ends with a curly bracket (}). That tells us that this information is a dictionary. To access documents in the collection, we'll use two methods: find and find_one. As you might expect, find will retrieve all the documents, and find_one will bring back only the first document. For now, let's stick to find_one; we'll come back to find later.

    Just like everywhere else, we'll need to assign a variable name to whatever comes back, so let's call this one result.

    [9]:
    xxxxxxxxxx
     
    result = lagos.find_one({})
    pp.pprint(result)
    {    '_id': ObjectId('6334b0f18c51459f9b1d955d'),
         'metadata': {    'lat': 6.501,
                          'lon': 3.367,
                          'measurement': 'temperature',
                          'sensor_id': 10,
                          'sensor_type': 'DHT11',
                          'site': 4},
         'temperature': nan,
         'timestamp': datetime.datetime(2018, 1, 7, 7, 7, 3, 88000)}
    
    xxxxxxxxxx
    ### Key-Value Pairs

    Key-Value Pairs¶

    There's a lot going on here! Let's work from the bottom up, starting with this:

    {
        'temperature': 27.0,
        'timestamp': datetime.datetime(2017, 9, 6, 13, 18, 10, 120000)
    }
    

    The actual data is labeled temperature and timestamp, and if seeing it presented this way seems familiar, that's because what you're seeing at the bottom are two key-value pairs. In PyMongo, "_id" is always the primary key. Primary keys are the column(s) which contain values that uniquely identify each row in a table; we'll talk about that more in a minute.

    xxxxxxxxxx
    ### Metadata

    Metadata¶

    Next, we have this:

    'metadata': { 'lat': 6.602,
                  'lon': 3.351,
                  'measurement': 'temperature',
                  'sensor_id': 9,
                  'sensor_type': 'DHT11',
                  'site': 2}
    

    This is the document's metadata. Metadata is data about the data. If you’re working with a database, its data is the information it contains, and its metadata describes what that information is. In MongoDB, each document often has metadata of its own. If we go back to the example of your email account, each message in your Sent folder includes both the message itself and information about when you sent it and who you sent it to; the message is data, and the other information is metadata.

    The metadata we see in this block of code tells us what the key-value pairs from the last code block mean, and where the information stored there comes from. There's location data, a line telling us what about the format of the key-value pairs, some information about the equipment used to gather the data, and where the data came from.

    xxxxxxxxxx
    ### Identifiers

    Identifiers¶

    Finally, at the top, we have this:

    { 
        '_id': ObjectId('6126f1780e45360640bf240a')
    }
    

    This is the document's unique identifier, which is similar to the index label for each row in a pandas DataFrame.

    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Retrieve a single document from the "nairobi" collection, and print the result.

    [10]:
    xxxxxxxxxx
     
    result = nairobi.find_one({})
    pp.pprint(result)
    {    'P1': 39.67,
         '_id': ObjectId('6334b0e98c51459f9b198d27'),
         'metadata': {    'lat': -1.3,
                          'lon': 36.785,
                          'measurement': 'P1',
                          'sensor_id': 57,
                          'sensor_type': 'SDS011',
                          'site': 29},
         'timestamp': datetime.datetime(2018, 9, 1, 0, 0, 2, 472000)}
    
    xxxxxxxxxx
    ## Analyzing Data

    Analyzing Data¶

    Now that we've seen what a document looks like in this collection, let's start working with what we've got. Since our metadata includes information about each sensor's "site", we might be curious to know how many sites are in the "lagos" collection. To do that, we'll use the distinct method, like this:

    [11]:
    xxxxxxxxxx
     
    lagos.distinct("metadata.site")
    [11]:
    [3, 4]
    xxxxxxxxxx
    Notice that in order to grab the `"site"` number, we needed to include the `"metadata"` tag. 

    Notice that in order to grab the "site" number, we needed to include the "metadata" tag.

    This tells us that there are 2 sensor sites in Lagos: one labeled 3 and the other labeled 4.

    Let's go further. We know that there are two sensor sites in Lagos, but we don't know how many documents are associated with each site. To find that out, we'll use the count_documents method for each site.

    [12]:
    xxxxxxxxxx
     
    print("Documents from site 3:", lagos.count_documents({"metadata.site": 3}))
    print("Documents from site 4:", lagos.count_documents({"metadata.site": 4}))
    Documents from site 3: 140586
    Documents from site 4: 25910
    
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Find out how many sensor sites are in Nairobi, what their labels are, and how many documents are associated with each one.

    [13]:
    xxxxxxxxxx
     
    nairobi.distinct("metadata.site")
    print("Documents from site 29:", nairobi.count_documents({"metadata.site":29}))
    print("Documents from site 6:", nairobi.count_documents({"metadata.site":6}))
    Documents from site 29: 131852
    Documents from site 6: 70360
    
    [14]:
    xxxxxxxxxx
     
    print("Documents from site 29:", nairobi.count_documents({"metadata.site": 29}))
    print("Documents from site 6:", nairobi.count_documents({"metadata.site": 6}))
    # REMOVE}
    Documents from site 29: 131852
    Documents from site 6: 70360
    
    xxxxxxxxxx
    Now that we know how many *documents* are associated with each site, let's keep drilling down and find the number of *readings* for each site. We'll do this with the [`aggregate`](https://pymongo.readthedocs.io/en/stable/api/pymongo/collection.html#pymongo.collection.Collection.aggregate) method.

    Now that we know how many documents are associated with each site, let's keep drilling down and find the number of readings for each site. We'll do this with the aggregate method.

    Before we run it, let's take a look at some of what's happening in the code here. First, you'll notice that there are several dollar signs ($) in the list. This is telling the collection that we want to create something new. Here, we're saying that we want there to be a new group, and that the new group needs to be updated with data from metadata.site, and then updated again with data from count.

    There's also a new field: "_id". In PyMongo, "_id" is always the primary key. Primary keys are the fields which contain values that uniquely identify each row in a table.

    Let's run the code and see what happens:

    [15]:
    xxxxxxxxxx
     
    result = lagos.aggregate(
        # Here's the `$` and the `"_id"`
        [{"$group": {"_id": "$metadata.site", "count": {"$count": {}}}}]
    )
    pp.pprint(list(result))
    [{'_id': 3, 'count': 140586}, {'_id': 4, 'count': 25910}]
    
    xxxxxxxxxx
    With that information in mind, we might want to know what those readings actually are. Since we're really interested in measures of air quality, let's take a look at the `P2` values in the `"lagos"` collection. `P2` measures the amount of particulate matter in the air, which in this case is something called PM 2.5. If we wanted to get all the documents in a collection, we could, but that would result in an unmanageably large number of records clogging up the memory on our machines. Instead, let's use the [`find`](https://pymongo.readthedocs.io/en/stable/api/pymongo/collection.html#pymongo.collection.Collection.find) method and use `limit` to make sure we only get back the first 3. 

    With that information in mind, we might want to know what those readings actually are. Since we're really interested in measures of air quality, let's take a look at the P2 values in the "lagos" collection. P2 measures the amount of particulate matter in the air, which in this case is something called PM 2.5. If we wanted to get all the documents in a collection, we could, but that would result in an unmanageably large number of records clogging up the memory on our machines. Instead, let's use the find method and use limit to make sure we only get back the first 3.

    [16]:
    xxxxxxxxxx
     
    result = lagos.find({"metadata.measurement": "P2"}).limit(3)
    pp.pprint(list(result))
    [    {    'P2': 14.42,
              '_id': ObjectId('6334b0f28c51459f9b1de145'),
              'metadata': {    'lat': 6.501,
                               'lon': 3.367,
                               'measurement': 'P2',
                               'sensor_id': 6,
                               'sensor_type': 'PPD42NS',
                               'site': 4},
              'timestamp': datetime.datetime(2018, 1, 7, 7, 7, 3, 39000)},
         {    'P2': 19.66,
              '_id': ObjectId('6334b0f28c51459f9b1de146'),
              'metadata': {    'lat': 6.501,
                               'lon': 3.367,
                               'measurement': 'P2',
                               'sensor_id': 6,
                               'sensor_type': 'PPD42NS',
                               'site': 4},
              'timestamp': datetime.datetime(2018, 1, 7, 7, 11, 23, 870000)},
         {    'P2': 24.79,
              '_id': ObjectId('6334b0f28c51459f9b1de147'),
              'metadata': {    'lat': 6.501,
                               'lon': 3.367,
                               'measurement': 'P2',
                               'sensor_id': 6,
                               'sensor_type': 'PPD42NS',
                               'site': 4},
              'timestamp': datetime.datetime(2018, 1, 7, 7, 21, 53, 981000)}]
    
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Find out how many sensor sites are in Nairobi, what their labels are, how many documents are associated with each one, and the number of observations from each site. Then, return the first three documents with the value P2.

    [17]:
    xxxxxxxxxx
     
    result = nairobi.aggregate(
        # Here's the `$` and the `"_id"`
        [{"$group": {"_id": "$metadata.site", "count": {"$count": {}}}}]
    )
    pp.pprint(list(result))
    result = lagos.find({"metadata.measurement": "P2"}).limit(3)
    pp.pprint(list(result))
    [{'_id': 6, 'count': 70360}, {'_id': 29, 'count': 131852}]
    [    {    'P2': 14.42,
              '_id': ObjectId('6334b0f28c51459f9b1de145'),
              'metadata': {    'lat': 6.501,
                               'lon': 3.367,
                               'measurement': 'P2',
                               'sensor_id': 6,
                               'sensor_type': 'PPD42NS',
                               'site': 4},
              'timestamp': datetime.datetime(2018, 1, 7, 7, 7, 3, 39000)},
         {    'P2': 19.66,
              '_id': ObjectId('6334b0f28c51459f9b1de146'),
              'metadata': {    'lat': 6.501,
                               'lon': 3.367,
                               'measurement': 'P2',
                               'sensor_id': 6,
                               'sensor_type': 'PPD42NS',
                               'site': 4},
              'timestamp': datetime.datetime(2018, 1, 7, 7, 11, 23, 870000)},
         {    'P2': 24.79,
              '_id': ObjectId('6334b0f28c51459f9b1de147'),
              'metadata': {    'lat': 6.501,
                               'lon': 3.367,
                               'measurement': 'P2',
                               'sensor_id': 6,
                               'sensor_type': 'PPD42NS',
                               'site': 4},
              'timestamp': datetime.datetime(2018, 1, 7, 7, 21, 53, 981000)}]
    
    xxxxxxxxxx
    So far, we've been dealing with relatively small subsets of the data in our collections, but what if we need to work with something bigger? Let's start by using `distinct` to remind ourselves of the kinds of data we have at our disposal.

    So far, we've been dealing with relatively small subsets of the data in our collections, but what if we need to work with something bigger? Let's start by using distinct to remind ourselves of the kinds of data we have at our disposal.

    [18]:
    xxxxxxxxxx
     
    lagos.distinct("metadata.measurement")
    [18]:
    ['humidity', 'temperature', 'P1', 'P2']
    xxxxxxxxxx
    There are also comparison query operators that can be helpful for filtering the data. In total, we have 

    There are also comparison query operators that can be helpful for filtering the data. In total, we have

    • $gt: greater than (>)
    • $lt: less than (<)
    • $gte: greater than equal to (>=)
    • $lte: less than equal to (<= )

    Let's use the timestamp to see how we can use these operators to select different documents:

    [19]:
    xxxxxxxxxx
     
    import datetime
    ​
    result = nairobi.find({"timestamp": {"$gt": datetime.datetime(2018, 9, 1)}}).limit(3)
    pp.pprint(list(result))
    [    {    'P1': 39.67,
              '_id': ObjectId('6334b0e98c51459f9b198d27'),
              'metadata': {    'lat': -1.3,
                               'lon': 36.785,
                               'measurement': 'P1',
                               'sensor_id': 57,
                               'sensor_type': 'SDS011',
                               'site': 29},
              'timestamp': datetime.datetime(2018, 9, 1, 0, 0, 2, 472000)},
         {    'P1': 39.13,
              '_id': ObjectId('6334b0e98c51459f9b198d28'),
              'metadata': {    'lat': -1.3,
                               'lon': 36.785,
                               'measurement': 'P1',
                               'sensor_id': 57,
                               'sensor_type': 'SDS011',
                               'site': 29},
              'timestamp': datetime.datetime(2018, 9, 1, 0, 5, 3, 941000)},
         {    'P1': 30.07,
              '_id': ObjectId('6334b0e98c51459f9b198d29'),
              'metadata': {    'lat': -1.3,
                               'lon': 36.785,
                               'measurement': 'P1',
                               'sensor_id': 57,
                               'sensor_type': 'SDS011',
                               'site': 29},
              'timestamp': datetime.datetime(2018, 9, 1, 0, 10, 4, 374000)}]
    
    [20]:
    xxxxxxxxxx
     
    result = nairobi.find({"timestamp": {"$lt": datetime.datetime(2018, 12, 1)}}).limit(3)
    pp.pprint(list(result))
    [    {    'P1': 39.67,
              '_id': ObjectId('6334b0e98c51459f9b198d27'),
              'metadata': {    'lat': -1.3,
                               'lon': 36.785,
                               'measurement': 'P1',
                               'sensor_id': 57,
                               'sensor_type': 'SDS011',
                               'site': 29},
              'timestamp': datetime.datetime(2018, 9, 1, 0, 0, 2, 472000)},
         {    'P1': 39.13,
              '_id': ObjectId('6334b0e98c51459f9b198d28'),
              'metadata': {    'lat': -1.3,
                               'lon': 36.785,
                               'measurement': 'P1',
                               'sensor_id': 57,
                               'sensor_type': 'SDS011',
                               'site': 29},
              'timestamp': datetime.datetime(2018, 9, 1, 0, 5, 3, 941000)},
         {    'P1': 30.07,
              '_id': ObjectId('6334b0e98c51459f9b198d29'),
              'metadata': {    'lat': -1.3,
                               'lon': 36.785,
                               'measurement': 'P1',
                               'sensor_id': 57,
                               'sensor_type': 'SDS011',
                               'site': 29},
              'timestamp': datetime.datetime(2018, 9, 1, 0, 10, 4, 374000)}]
    
    [21]:
    xxxxxxxxxx
     
    result = nairobi.find(
        {"timestamp": {"$eq": datetime.datetime(2018, 9, 1, 0, 0, 2, 472000)}}
    ).limit(3)
    pp.pprint(list(result))
    [    {    'P1': 39.67,
              '_id': ObjectId('6334b0e98c51459f9b198d27'),
              'metadata': {    'lat': -1.3,
                               'lon': 36.785,
                               'measurement': 'P1',
                               'sensor_id': 57,
                               'sensor_type': 'SDS011',
                               'site': 29},
              'timestamp': datetime.datetime(2018, 9, 1, 0, 0, 2, 472000)},
         {    'P2': 34.43,
              '_id': ObjectId('6334b0ea8c51459f9b1a0db2'),
              'metadata': {    'lat': -1.3,
                               'lon': 36.785,
                               'measurement': 'P2',
                               'sensor_id': 57,
                               'sensor_type': 'SDS011',
                               'site': 29},
              'timestamp': datetime.datetime(2018, 9, 1, 0, 0, 2, 472000)}]
    
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Find three documents with timestamp greater than or equal to and less than or equal the date December 12, 2018 — datetime.datetime(2018, 12, 1, 0, 0, 6, 767000).

    [22]:
    xxxxxxxxxx
     
    # Greater than or equal to
    ​
    result = nairobi.find({"timestamp":{"$gte":datetime.datetime(2018,12,1,0,0,6,767000)}}).limit(3)
    ​
    pp.pprint(list(result))
    [    {    'P1': 17.08,
              '_id': ObjectId('6334b0e98c51459f9b19eba8'),
              'metadata': {    'lat': -1.3,
                               'lon': 36.785,
                               'measurement': 'P1',
                               'sensor_id': 57,
                               'sensor_type': 'SDS011',
                               'site': 29},
              'timestamp': datetime.datetime(2018, 12, 1, 0, 0, 6, 767000)},
         {    'P1': 17.62,
              '_id': ObjectId('6334b0e98c51459f9b19eba9'),
              'metadata': {    'lat': -1.3,
                               'lon': 36.785,
                               'measurement': 'P1',
                               'sensor_id': 57,
                               'sensor_type': 'SDS011',
                               'site': 29},
              'timestamp': datetime.datetime(2018, 12, 1, 0, 5, 6, 327000)},
         {    'P1': 11.05,
              '_id': ObjectId('6334b0e98c51459f9b19ebaa'),
              'metadata': {    'lat': -1.3,
                               'lon': 36.785,
                               'measurement': 'P1',
                               'sensor_id': 57,
                               'sensor_type': 'SDS011',
                               'site': 29},
              'timestamp': datetime.datetime(2018, 12, 1, 0, 10, 5, 579000)}]
    
    [23]:
    xxxxxxxxxx
     
    # Less than or equal to
    ​
    result = ...
    ​
    pp.pprint(list(result))
    ---------------------------------------------------------------------------
    TypeError                                 Traceback (most recent call last)
    Cell In [23], line 5
          1 # Less than or equal to
          3 result = ...
    ----> 5 pp.pprint(list(result))
    
    TypeError: 'ellipsis' object is not iterable
    xxxxxxxxxx
    ## Updating Documents

    Updating Documents¶

    xxxxxxxxxx
    We can also update documents by passing some filter and new values using `update_one` to update one record or `update_many` to update many records. Let's look at an example. Before updating, we have this record showing like this:

    We can also update documents by passing some filter and new values using update_one to update one record or update_many to update many records. Let's look at an example. Before updating, we have this record showing like this:

    [ ]:
    xxxxxxxxxx
     
    result = nairobi.find(
        {"timestamp": {"$eq": datetime.datetime(2018, 9, 1, 0, 0, 2, 472000)}}
    ).limit(1)
    ​
    pp.pprint(list(result))
    xxxxxxxxxx
    Now we are updating the sensor type from `"SDS011"` to `"SDS"`, we first select all records with sensor type equal to `"SDS011"`, then set the new value to `"SDS"`:

    Now we are updating the sensor type from "SDS011" to "SDS", we first select all records with sensor type equal to "SDS011", then set the new value to "SDS":

    [ ]:
    xxxxxxxxxx
     
    result = nairobi.update_many(
        {"metadata.sensor_type": {"$eq": "SDS101"}},
        {"$set": {"metadata.sensor_type": "SDS"}},
    )
    xxxxxxxxxx
    Now we can see all records have changed:

    Now we can see all records have changed:

    [ ]:
    xxxxxxxxxx
     
    result = nairobi.find(
        {"timestamp": {"$eq": datetime.datetime(2018, 9, 1, 0, 0, 2, 472000)}}
    ).limit(3)
    ​
    pp.pprint(list(result))
    xxxxxxxxxx
    We can change it back:

    We can change it back:

    [ ]:
    xxxxxxxxxx
     
    result = nairobi.update_many(
        {"metadata.sensor_type": {"$eq": "SDS"}},
        {"$set": {"metadata.sensor_type": "SDS101"}},
    )
    [ ]:
    xxxxxxxxxx
     
    result.raw_result
    xxxxxxxxxx
    ## Aggregation

    Aggregation¶

    xxxxxxxxxx
    Since we're looking for *big* numbers, we need to figure out which one of those dimensions has the largest number of measurements by **aggregating** the data in each document. Since we already know that `site 3` has significantly more documents than `site 2`, let's start looking at `site 3`. We can use the `$match` syntax to only select `site 3` data. The code to do that looks like this: 

    Since we're looking for big numbers, we need to figure out which one of those dimensions has the largest number of measurements by aggregating the data in each document. Since we already know that site 3 has significantly more documents than site 2, let's start looking at site 3. We can use the $match syntax to only select site 3 data. The code to do that looks like this:

    [ ]:
    xxxxxxxxxx
     
    result = lagos.aggregate(
        [
            {"$match": {"metadata.site": 3}},  # `3` is the site number.
            {"$group": {"_id": "$metadata.measurement", "count": {"$count": {}}}},
        ]
    )
    pp.pprint(list(result))
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Find the number of each measurement type at site 29 in Nairobi.

    [ ]:
    xxxxxxxxxx
     
    result = ...
    pp.pprint(list(result))
    xxxxxxxxxx
    After aggregation, there is another useful operator called `$project`, which allows you to specify which fields to display by adding new fields or deleting fields. Using the Nairobi data from site 29, we can first count each sensor type:<span style='color: transparent; font-size:1%'>WQU WorldQuant University Applied Data Science Lab QQQQ</span>

    After aggregation, there is another useful operator called $project, which allows you to specify which fields to display by adding new fields or deleting fields. Using the Nairobi data from site 29, we can first count each sensor type:WQU WorldQuant University Applied Data Science Lab QQQQ

    [ ]:
    xxxxxxxxxx
     
    result = nairobi.aggregate(
        [
            {"$match": {"metadata.site": 29}},
            {"$group": {"_id": "$metadata.sensor_type", "count": {"$count": {}}}},
        ]
    )
    ​
    pp.pprint(list(result))
    xxxxxxxxxx
    We can see there are two sensor types and the corresponding counts. If we only want to display what are the types but do not care about the counts, we can suppress the `count` filed by setting it at 0 in `$project`:

    We can see there are two sensor types and the corresponding counts. If we only want to display what are the types but do not care about the counts, we can suppress the count filed by setting it at 0 in $project:

    [ ]:
    xxxxxxxxxx
     
    result = nairobi.aggregate(
        [
            {"$match": {"metadata.site": 29}},
            {"$group": {"_id": "$metadata.sensor_type", "count": {"$count": {}}}},
            {"$project": {"count": 0}},
        ]
    )
    ​
    pp.pprint(list(result))
    xxxxxxxxxx
    The `$project` syntax is also useful for deleting the intermediate fields that we used to generate our final fields but no longer need. In the following example, let's calculate the date difference for each sensor type. We'll first use the aggregation method to get the start date and last date. 

    The $project syntax is also useful for deleting the intermediate fields that we used to generate our final fields but no longer need. In the following example, let's calculate the date difference for each sensor type. We'll first use the aggregation method to get the start date and last date.

    [ ]:
    xxxxxxxxxx
     
    result = nairobi.aggregate(
        [
            {"$match": {"metadata.site": 29}},
            {
                "$group": {
                    "_id": "$metadata.sensor_type",
                    "date_min": {"$min": "$timestamp"},
                    "date_max": {"$max": "$timestamp"},
                }
            },
        ]
    )
    ​
    pp.pprint(list(result))
    xxxxxxxxxx
    Then we can calculate the date difference using `$dateDiff`, which gets the date difference through specifying the start date, end date and unit for timestamp data. We can see from the results above that the dates, are very close to each other. The only differences are in the minutes, so we can specify the unit as minute to show the difference. Since we don't need the start date and end dates, we can define a `"dateDiff"` field inside `$project`, so that it will be shown in the final display: 

    Then we can calculate the date difference using $dateDiff, which gets the date difference through specifying the start date, end date and unit for timestamp data. We can see from the results above that the dates, are very close to each other. The only differences are in the minutes, so we can specify the unit as minute to show the difference. Since we don't need the start date and end dates, we can define a "dateDiff" field inside $project, so that it will be shown in the final display:

    [ ]:
    xxxxxxxxxx
     
    result = nairobi.aggregate(
        [
            {"$match": {"metadata.site": 29}},
            {
                "$group": {
                    "_id": "$metadata.sensor_type",
                    "date_min": {"$min": "$timestamp"},
                    "date_max": {"$max": "$timestamp"},
                }
            },
            {
                "$project": {
                    "dateDiff": {
                        "$dateDiff": {
                            "startDate": "$date_min",
                            "endDate": "$date_max",
                            "unit": "minute",
                        }
                    }
                }
            },
        ]
    )
    ​
    pp.pprint(list(result))
    xxxxxxxxxx
    If we specify unit as `day`, it will show the difference between the dates:

    If we specify unit as day, it will show the difference between the dates:

    [ ]:
    xxxxxxxxxx
     
    result = nairobi.aggregate(
        [
            {"$match": {"metadata.site": 29}},
            {
                "$group": {
                    "_id": "$metadata.sensor_type",
                    "date_min": {"$min": "$timestamp"},
                    "date_max": {"$max": "$timestamp"},
                }
            },
            {
                "$project": {
                    "dateDiff": {
                        "$dateDiff": {
                            "startDate": "$date_min",
                            "endDate": "$date_max",
                            "unit": "day",
                        }
                    }
                }
            },
        ]
    )
    ​
    pp.pprint(list(result))
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself find the date difference for each measurement type at site 29 in Nairobi.

    [ ]:
    xxxxxxxxxx
     
    result = ...
    ​
    pp.pprint(list(result))
    xxxxxxxxxx
    We can do more with the date data using `$dateTrunc`, which truncates datetime data. We need to specify the datetime data, which can be a `Date`, a `Timestamp`, or an `ObjectID`. Then we need to specify the `unit` (year, month, day, hour, minute, second) and `binSize` (numerical variable defining the size of the truncation). Let's check the example below, where we group data by the month using `$dateTrunc` and then count how many observations there are for each month.

    We can do more with the date data using $dateTrunc, which truncates datetime data. We need to specify the datetime data, which can be a Date, a Timestamp, or an ObjectID. Then we need to specify the unit (year, month, day, hour, minute, second) and binSize (numerical variable defining the size of the truncation). Let's check the example below, where we group data by the month using $dateTrunc and then count how many observations there are for each month.

    [ ]:
    xxxxxxxxxx
     
    result = nairobi.aggregate(
        [
            {"$match": {"metadata.site": 29}},
            {
                "$group": {
                    "_id": {
                        "truncatedDate": {
                            "$dateTrunc": {
                                "date": "$timestamp",
                                "unit": "month",
                                "binSize": 1,
                            }
                        }
                    },
                    "count": {"$count": {}},
                }
            },
        ]
    )
    pp.pprint(list(result))
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Truncate date by week and count at site 29 in Nairobi.

    [ ]:
    xxxxxxxxxx
     
    result = ...
    ​
    pp.pprint(list(result))
    xxxxxxxxxx
    ## Finishing Up

    Finishing Up¶

    So far, we've connected to a server, accessed that server with a client, found the collection we were looking for within a database, and explored that collection in all sorts of different ways. Now it's time to get the data we'll actually need to build a model, and store that in a way we'll be able to use.

    Let's use find to retrieve the PM 2.5 data from site 3. And, since we don't need any of the metadata to build our model, let's strip that out using the projection argument. In this case, we're telling the collection that we only want to see "timestamp" and "P2". Keep in mind that we limited the number of records we'll get back to 3 when we defined result above.

    [ ]:
    xxxxxxxxxx
     
    result = lagos.find(
        {"metadata.site": 3, "metadata.measurement": "P2"},
        # `projection` limits the kinds of data we'll get back.
        projection={"P2": 1, "timestamp": 1, "_id": 0},
    )
    pp.pprint(result.next())
    xxxxxxxxxx
    Finally, we'll use pandas to read the extracted data into a DataFrame, making sure to set `timestamp` as the index:

    Finally, we'll use pandas to read the extracted data into a DataFrame, making sure to set timestamp as the index:

    [ ]:
    xxxxxxxxxx
     
    df = pd.DataFrame(result).set_index("timestamp")
    df.head()
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Retrieve the PM 2.5 data from site 29 in Nairobi and strip out the metadata to create a DataFrame that shows only timestamp and P2. Print the result.

    [ ]:
    xxxxxxxxxx
     
    result = ...
    df = ...
    df.head()
    xxxxxxxxxx
    # References & Further Reading

    References & Further Reading¶

    • Further reading about servers and clients
    • Definitions from the MongoDB documentation
    • Information on Iterators
    • MongoDB documentation in Aggregation
    xxxxxxxxxx
    ---

    Copyright 2022 WorldQuant University. This content is licensed solely for personal use. Redistribution or publication of this material is strictly prohibited.

    xxxxxxxxxx
    Advanced Tools
    xxxxxxxxxx
    xxxxxxxxxx

    -

    Variables

    Callstack

      Breakpoints

      Source

      xxxxxxxxxx
      1
      11-databases-mongodb.ipynb
      • Working with PyMongo
      • Databases
      • Servers and Clients
      • Semi-structured Data
      • Exploring a Database
      • Collections
      • Documents
      • Retrieving Data
      • Key-Value Pairs
      • Metadata
      • Identifiers
      • Analyzing Data
      • Updating Documents
      • Aggregation
      • Finishing Up
      • References & Further Reading
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